A Hybrid PAPR Reduction Scheme for OFDM Systems Using Perfect Sequences
Bibliographic record
Abstract
In this paper, a peak to average power ratio (PAPR) reduction scheme with low complexity and high performance for orthogonal frequency division multiplexing (OFDM) signals is proposed. The proposed scheme is a hybrid PAPR scheme that employs a two-stage cascade structure. The first stage is a post-IFFT stage that can construct a set of high-order quadrature amplitude modulation (QAM) sequences from QPSK or BPSK sequences with the smallest possible number of IFFTs. The second stage is based on an optimal Class-III selected mapping (SLM) scheme which consists of a bank of parallel blocks. Each of these blocks generates more candidate sequences from each of QAM sequences by passing it through a set of parallel sub-blocks that perform circular convolution with perfect sequences and circular shifting with optimum shift values. Simulation results show that the proposed scheme can outperform existing schemes in terms of PAPR reduction with lower complexity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".